{
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      "metadata": {},
      "source": [
        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/cohere-ai/notebooks/blob/main/notebooks/llmu/Embed_Endpoint.ipynb\">\n",
        "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
        "</a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_pmaIlTcugAd"
      },
      "source": [
        "# The Embed Endpoint\n",
        "\n",
        "In this lab, we'll learn how to analyze a text dataset using Cohere's Embed cohere endpoint. This colab accompanies the [Classify endpoint lesson](https://docs.cohere.com/docs/embed-endpoint/) of LLM University."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ys2CHEgurfe"
      },
      "source": [
        "# Setting Up\n",
        "The first step is to install the Cohere Python SDK. Next, create an API key, which you can generate from the Cohere [dashboard](https://os.cohere.ai/register) or [CLI tool](https://docs.cohere.ai/cli-key)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "QdEURifRRUgy"
      },
      "outputs": [],
      "source": [
        "# Install the libraries\n",
        "# TODO: upgrade to \"cohere>5\"\n",
        "! pip install cohere altair umap-learn -q"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "YYi9JxxFRWX7"
      },
      "outputs": [],
      "source": [
        "# Import the libraries\n",
        "import cohere\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import altair as alt\n",
        "import textwrap as tr\n",
        "\n",
        "# Setup the Cohere client\n",
        "co = cohere.Client(\"COHERE_API_KEY\") # Get your API key here: https://dashboard.cohere.com/api-keys"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4un3lbJmRxNz"
      },
      "source": [
        "# Analyzing Text"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "abSwokLuAofs"
      },
      "source": [
        "Cohere’s Embed endpoint takes a piece of text and turns it into a vector embedding. Embeddings represent text in the form of numbers that capture its meaning and context. What it means is that it gives you the ability to turn unstructured text data into a structured form. It opens up ways to analyze and extract insights from them.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lr-kAdCSoqmK"
      },
      "source": [
        "## Get embeddings"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_2xklTNZA5iC"
      },
      "source": [
        "Here we have a list of 50 top web search terms about Hello, World! taken from a keyword tool. Let’s look at a few examples:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
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          "height": 206
        },
        "id": "XEqrl7yoouG7",
        "outputId": "b6edc12a-e716-4911-dbc1-6cbd91d638d2"
      },
      "outputs": [
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            "text/plain": [
              "                                    search_term\n",
              "0            how to print hello world in python\n",
              "1                           what is hello world\n",
              "2  how do you write hello world in an alert box\n",
              "3              how to print hello world in java\n",
              "4           how to write hello world in eclipse"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Get a list of texts and add to a dataframe\n",
        "df = pd.read_csv(\"https://github.com/cohere-ai/notebooks/raw/main/notebooks/data/hello-world-kw.csv\", names=[\"search_term\"])\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x_CmphwPBI4S"
      },
      "source": [
        "We use the Embed endpoint to get the embeddings for each of these serach terms."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "a0wH95UrpBqf"
      },
      "outputs": [],
      "source": [
        "# A function that classifies a list of inputs given the examples\n",
        "def embed_text(texts):\n",
        "  \"\"\"\n",
        "  Turns a piece of text into embeddings\n",
        "  Arguments:\n",
        "    text(str): the text to be turned into embeddings\n",
        "  Returns:\n",
        "    embedding(list): the embeddings\n",
        "  \"\"\"\n",
        "  # Embed text by calling the Embed endpoint\n",
        "  output = co.embed(\n",
        "                model=\"embed-english-v3.0\",\n",
        "                input_type=\"search_document\",\n",
        "                texts=texts)\n",
        "  embedding = output.embeddings\n",
        "\n",
        "  return embedding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "nDfgCFclMYI3"
      },
      "outputs": [],
      "source": [
        "# Get embeddings of all search terms\n",
        "df[\"search_term_embeds\"] = embed_text(df[\"search_term\"].tolist())\n",
        "embeds = np.array(df[\"search_term_embeds\"].tolist())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EwboOr4qR284"
      },
      "source": [
        "### Semantic Search\n",
        "\n",
        "**Note:** This semantic search section is not contained in the blog post, but we encourage you to still check it out if you'd like to see how the embedding is used for searching in the dataset!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TcH4RUtFBVcQ"
      },
      "source": [
        "We’ll look at a couple of example applications. The first example is semantic search. Given a new query, our \"search engine\" must return the most similar FAQs, where the FAQs are the 50 search terms we uploaded earlier.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "aCvHJMGupz_Y"
      },
      "outputs": [],
      "source": [
        "# Add a new query\n",
        "new_query = \"what is the history of hello world\"\n",
        "\n",
        "# Get embeddings of the new query\n",
        "new_query_embeds = embed_text([new_query])[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "inIpSZMUB7rz"
      },
      "source": [
        "We use cosine similarity to compare the similarity of the new query with each of the FAQs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "Pz_zMHh5pyVN"
      },
      "outputs": [],
      "source": [
        "# Calculate cosine similarity\n",
        "\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "\n",
        "def get_similarity(target, candidates):\n",
        "  \"\"\"\n",
        "  Computes the similarity between a target text and a list of other texts\n",
        "  Arguments:\n",
        "    target(list[float]): the target text\n",
        "    candidates(list[list[float]]): a list of other texts, or candidates\n",
        "  Returns:\n",
        "    sim(list[tuple]): candidate IDs and the similarity scores\n",
        "  \"\"\"\n",
        "  # Turn list into array\n",
        "  candidates = np.array(candidates)\n",
        "  target = np.expand_dims(np.array(target),axis=0)\n",
        "\n",
        "  # Calculate cosine similarity\n",
        "  sim = cosine_similarity(target,candidates)\n",
        "  sim = np.squeeze(sim).tolist()\n",
        "\n",
        "  # Sort by descending order in similarity\n",
        "  sim = list(enumerate(sim))\n",
        "  sim = sorted(sim, key=lambda x:x[1], reverse=True)\n",
        "\n",
        "  # Return similarity scores\n",
        "  return sim"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0zM-Z_3zC-xJ"
      },
      "source": [
        "Finally, we display the top 5 FAQs that match the new query"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vtnMXIqzo72i",
        "outputId": "317f0cfd-8ac1-4b7e-cf10-b439dc7a204a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "New query:\n",
            "what is the history of hello world \n",
            "\n",
            "Similar queries:\n",
            "Similarity: 0.86; how did hello world originate\n",
            "Similarity: 0.84; what is hello world\n",
            "Similarity: 0.83; how to do hello world\n",
            "Similarity: 0.81; why hello world\n",
            "Similarity: 0.81; where did hello world come from\n"
          ]
        }
      ],
      "source": [
        "# Get the similarity between the new query and existing queries\n",
        "similarity = get_similarity(new_query_embeds,embeds)\n",
        "\n",
        "# Display the top 5 FAQs\n",
        "print(\"New query:\")\n",
        "print(new_query,'\\n')\n",
        "\n",
        "print(\"Similar queries:\")\n",
        "for idx,score in similarity[:5]:\n",
        "  print(f\"Similarity: {score:.2f};\", df.iloc[idx][\"search_term\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2c3lK3bCR42j"
      },
      "source": [
        "### Semantic Exploration"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "23MbL2j3DIbY"
      },
      "source": [
        "In the second example, we take the same idea as semantic search and take a broader look, which is exploring huge volumes of text and analyzing their semantic relationships.\n",
        "\n",
        "We'll use the same 50 top web search terms about Hello, World! There are different techniques we can use to compress the embeddings down to just 2 dimensions while retaining as much information as possible. We'll use a technique called UMAP. And once we can get it down to 2 dimensions, we can plot these embeddings on a 2D chart."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "SS7QVt_GRskI"
      },
      "outputs": [],
      "source": [
        "# Reduce the embeddings' dimensions to 2 using UMAP\n",
        "import umap\n",
        "reducer = umap.UMAP(n_neighbors=49)\n",
        "umap_embeds = reducer.fit_transform(embeds)\n",
        "\n",
        "# Add the 2 dimensions to the dataframe\n",
        "df['x'] = umap_embeds[:,0]\n",
        "df['y'] = umap_embeds[:,1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 763
        },
        "id": "X132m02guyA1",
        "outputId": "85750bb9-20f9-4721-839c-a33792f2b226"
      },
      "outputs": [
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              "</script>"
            ],
            "text/plain": [
              "alt.LayerChart(...)"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Plot the 2-dimension embeddings on a chart\n",
        "chart = alt.Chart(df).mark_circle(size=500).encode(\n",
        "  x=\n",
        "  alt.X('x',\n",
        "      scale=alt.Scale(zero=False),\n",
        "      axis=alt.Axis(labels=False, ticks=False, domain=False)\n",
        "  ),\n",
        "\n",
        "  y=\n",
        "  alt.Y('y',\n",
        "      scale=alt.Scale(zero=False),\n",
        "      axis=alt.Axis(labels=False, ticks=False, domain=False)\n",
        "  ),\n",
        "\n",
        "  tooltip=['search_term']\n",
        "  )\n",
        "\n",
        "text = chart.mark_text(align='left', dx=15, size=12, color='black'\n",
        "          ).encode(text='search_term', color= alt.value('black'))\n",
        "\n",
        "result = (chart + text).configure(background=\"#FDF7F0\"\n",
        "      ).properties(\n",
        "      width=1000,\n",
        "      height=700,\n",
        "      title=\"2D Embeddings\"\n",
        "      )\n",
        "\n",
        "result.interactive()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5XTtVliyNVyO"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3.10.0 64-bit ('3.10.0')",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.11.4"
    },
    "vscode": {
      "interpreter": {
        "hash": "1fb8019e3560b882083e525615cf48e713d3a7345a15eb723d805e91aa410aac"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
